Yoga Pose Detection and Feedback System

Abstract: Human pose estimation plays a vital role in various domains, including fitness tracking, physiotherapy, sports performance analysis, and human-computer interaction. Accurate posture detection is essential to prevent injuries, improve physical activity performance, and aid rehabilitation processes. This research presents a real-time human pose detection and feedback system leveraging the MoveNet deep learning model and TensorFlow. The system captures live video streams using OpenCV, processes the frames with MoveNet to extract key joint positions, and applies an angle calculation module to evaluate movement accuracy. To enhance accessibility and usability, the system integrates a graphical user interface (GUI) built with Tkinter and a text-to-speech feedback mechanism to provide real-time guidance. The effectiveness of the system is validated through comparative analysis with standard pose models, ensuring that users receive real-time feedback on their posture deviations. The experimental results demonstrate high detection accuracy, rapid processing speeds, and enhanced user engagement, making it a viable solution for automated fitness coaching, physiotherapy monitoring, and interactive learning applications. Additionally, the system reduces the reliance on human instructors by offering automated posture correction, thereby democratizing access to professional-level movement assessment